

Humans routinely encounter new tools, foods, or animals, having no problem perceiving the novel objects as objects despite having never seen them before.
Unlike humans, current state-of-the-art detection and segmentation methods  [1]}, [2]}, [3]}, [4]}, [5]} have difficulty recognizing novel objects as objects because they are designed with a closed-world assumption. Their training aims to localize known (annotated) objects while regarding unknown (unannotated) objects as background. This causes the models to fail in locating novel objects and learning general objectness.
One way to deal with this challenge is to create a dataset with an exhaustive annotation of every single object in each image. However, creating such datasets is very expensive. In fact, many public datasets [6]}, [7]}, [8]} for object detection and instance segmentation do not label all objects in an image (Fig. REF ).
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